Augmenting mental models

The complexity of conceptualizing mental models has made Virtual Reality an interesting way to enhance communication and understanding between individuals working together on a project or idea. Here, the authors discuss practical applications of using VR for this purpose

Creating shared mental models: The support of visual language

Cooperative design involves multiple stakeholders that often hold different ideas of the problem, the ways to solve it, and to its solutions (i.e., mental models; MM). These differences can result in miscommunication, misunderstanding, slower decision making processes, and less chance on cooperative decisions. In order to facilitate the creation of a shared mental model (sMM), visual languages (VL) are often used. However, little scientific foundation is behind this choice. To determine whether or not this gut feeling is justified, a research was conducted in which various stakeholders had to cooperatively redesign a process chain, with and without VL. To determine whether or not a sMM was created, scores on agreement in individual MM, communication, and cooperation were analyzed. The results confirmed the assumption that VL can indeed play an important role in the creation of sMM and, hence, can aid the processes of cooperative design and engineering

Federated learning enables big data for rare cancer boundary detection.

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Author Correction: Federated learning enables big data for rare cancer boundary detection.

10.1038/s41467-023-36188-7NATURE COMMUNICATIONS14

Federated Learning Enables Big Data for Rare Cancer Boundary Detection

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing